Finally, the research of classifying and identifying individuals through their walking patterns is introduced. Alive biometrical features in dynamic human gait are adopted in the intelligent shoe system. Since gait data are dynamic, non-linear, stochastic, time-varying, noisy and multi-channel, we must select a modeling framework capable of dealing with these expected complexities in the data. Using the proposed machine learning methods, support vector machine (SVM) and hidden Markov models (HMMs), we build up probabilistic models that take the information of human walking patterns into account, and compare the overall similarity among human walking patterns of several wearers. / In this thesis, we will build intelligent shoes under the framework for capturing and analyzing dynamic human gait. Existing MEMS technology makes it possible to integrate all the sensors and circuits inside a small module. In designing our intelligent shoe system, we require the following key characteristics in our system: (1) It should be convenient to wear and socially acceptable. Thus, the sensors and electronic hardware installed should not substantially change the weight and weight balance of a typical shoe, lest it alters how an individual normally walks. (2) We want to analyze a user's motion in real-time through a wireless interface to a remote laptop or other computer; we will also incorporate on-shoe data logging hardware for off-line analysis. (3) Sensors that monitor gait motion conditions may need to be attached to the insoles, in closer proximity to the foot of users. In order to investigate the problem of capturing power parasitically from normal human-body-motion for use in personal electronics applications, we also plan to develop an electromechanical generator embedded within the shoe for parasitic power collection from heel strike. / Next, we can encode specific motions to control external devices through a wireless interface. This same system architecture that allows us to classify broad categories of motion also allows the intelligent shoe to act as a programmable, low-data rate control interface. We apply the system to several successful tasks based on this platform, especially the Shoe-Mouse. By using this interface, we can operate a device with our feet. / Then, we present potential use of machine learning techniques, in particular support vector machine (SVM), and the intelligent shoe platform to detect discrete stages in the cyclic motion of dynamic human gait, and construct an identifier of five discrete events that occur in a cyclic process for precise control of functional electrical stimulation (FES). With the information of when the legs are in each phase of a gait, the timing of specific gait phase can be assessed. / Huang, Bufu. / "September 2007." / Adviser: Yangsheng Xu. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4931. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 122-131). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

The incidence of falls is common in older adults and becomes more frequent with advancing age. Falls and injuries associated with falls are among the most debilitating and traumatic medical problems encountered by the elderly. There is evidence that indicates there may be a cause and effect relationship between specific gait parameters and falling. If a simple gait test can predict fallers, it could become a valuable tool for identifying individuals at high risk of falling. The purposes of this study were 1) to determine whether performance on the Functional Ambulation Profile (FAP) could accurately predict fallers and non-fallers in a prospective manner and 2) to identify gait parameters within the FAP that would best classify fallers. My hypothesis for this study include 1) the Functional Ambulation Profile (FAP) will accurately predict subjects as fallers and non-fallers and 2) of the five variables that comprise the FAP walking velocity, right and left step length:leg length ratio, step width will be the most powerful predictors of fall status. Two hundred twenty six subjects were evaluated using the GAITRite electronic walkway. Falls surveillance was conducted for 8 months after each participant's specific GAITRite testing date. Average height, weight and BMI were 161.9 �� 8.7 cm, 68.1 �� 16.7 kg, and 25.9 �� 4.1 kg/m��, respectively. Men and women were analyzed separately using analysis of variance, logistic regression and relative operating characteristic curves. There was no difference between fallers and non-fallers for any of the FAP variables for both the men and women. Regression results indicated the overall model for FAP to predict fallers was not statistically significant for either men or women, (p=0.706 and p=0.543, respectively). In addition, none of the five variables that make up the FAP was significant enough to be included in a stepwise logistic model, thus we were unable to develop an alternative model for predicting fallers based on gait variables. A secondary analysis found that the FAP was unable to distinguish multiple fallers (3 or more) from occasional and non-fallers in this same study population. The results of this study indicate that the FAP does not predict falls in independently living men and women over the age of 70. Further, none of the five gait variables that compose the FAP was a significant independent predictor of falls in this same population. Based on the results of this study we conclude that the FAP alone is not sufficient to predict risk of falling among older adults. Because of the complexity of all the physical, psychological and environmental elements that can lead to falling, tests based on only spatial and temporal gait characteristics do not appear to be good fall predictors for independent older adults. / Graduation date: 2003

Thesis (Ph. D.)--Drexel University, 2004. / Includes abstract and vita. Includes bibliographical references (leaves 73-79).

Thesis (M.S.)--Florida State University, 2003. / Advisor: Dr. Tonya Toole, Florida State University, College of Human Sciences, Dept. of Nutrition, Food, and Exercise Sciences. Title and description from dissertation home page (viewed Sept. 23, 2003). Includes bibliographical references.

No description available.

Thesis (M.App.Sc. in Physiotherapy)--University of South Australia, 1997.

Thesis (MPhysio)--University of South Australia, 1998

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (Ph. D.)--University of Missouri-Columbia, 1996. / Typescript. Vita. Includes bibliographical references (leaves 97-101). Also available on the Internet.

Thesis (M.S.)--Purdue University, 2004. / Includes bibliographical references (leaves 65-67). Also available online (PDF file) by a subscription to the set or by purchasing the individual file.